The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
One particularly significant example is the development of digital photography, in particular due to the development of online photo posting and sharing sites. Thus, as of September 2010, one of the leaders among these types of sites has exceeded 5 billion photos put online and is continuing to put thousands more online each day.
These digital objects are generally inventoried in databases combined with keywords and/or other technical descriptors (name, location, size, resolution, etc.). These keywords and descriptors make it possible to perform searches in the database and return the objects whose keywords correspond to the search criteria entered by the user in a search field.
However, currently, most search engines are primarily designed to look for text within webpages or files, and in particular in associated descriptive texts.
In the case where the stored objects are not textual in nature, for example such as photos, the associated keywords and descriptors take on considerable importance to make it possible to perform an effective search and return relevant results.
Many search engines exist to perform such searches, and many algorithms have been developed in order to optimize the relevance of the results of the searches.
Despite improved algorithms, a keyword search has intrinsic limitations, for example in particular due to the existence in human language of synonyms, homonyms, hierarchy within terms, and degrees of precision. Due to these limitations, the specific intention of the user's search beyond the first meaning of the keywords used remains unknown to the search engine.
In order to offset these limitations, most search engines allow users to perform an advanced search, in particular by using several keywords combined with each other by logic operators.
Such a search method is not, however, particularly easy for users and may, on some search engines, even go so far as to require quasi-programming skills to write a request, while not knowing whether that request may be correctly interpreted by the engine and lead to the desired result.
Various systems exist making it possible to facilitate the user's task and optimize searches.
Application WO 2012/127168 thus targets a method for refining search results providing a first response to this problem.
It should, however, be noted that the method covered by document WO 2012/127168 refines the results of a prior search, i.e., performs a sort, but the initial request step itself is not optimized. This is therefore a lost optimization step.
Furthermore, the elements eliminated during the initial request step and not returned in the initial results are not taken into account during refining and will not be able to be reintegrated into the results if necessary.
Thus, there is also a need to optimize the initial request step of a method for searching for objects in a database.
To that end, the system developed by Google is for example known for its image search service, which makes it possible to launch a search from a digital image before performing complementary searches by associating one or more keywords with it.
However, the search system only takes into account a single image that must first be added before any keywords.
Changing the image restarts the search and erases the keywords previously added.
Furthermore, adding an image automatically launches the first search based on the image alone. The keywords subsequently added will make it possible to refine the selection.
Thus, there is a need justifying the development of a method making it possible to further optimize searches for objects contained in a database, in particular in the initial requests.